The aim of this work is to respond to the need to rethink the behavior and motivation of employees in their relationship with managers and social groups, i.e., one’s main goal is based on increasing engagement in order to reach organizational goals and job workers satisfaction, a complex concept that is influenced by different causes. Indeed, in this work it is analyzed the impact of working conditions on job satisfaction. This is where attention is drawn to the concept of entropy, since we are not focusing on the value a variable can take, but on the effort that has been expended to obtain it. The idea of entropy comes from a principle of thermodynamics dealing with energy. It usually refers to the idea that everything in the universe eventually moves from order to disorder, and entropy is the measurement of that change, that is used here to understand and assess the workers behavior and motivation. The subsequent formal model is based on a set of logical structures for knowledge representation and reasoning that conform to the above entropic view, then leading to an Artificial Neural Network approach to computation, an archetypal that considers the motive behind the action.

A number of guidelines for Psychosocial Risk Management in organizations have been proposed in recent decades; however, some reviews on the subject also highlights that the terms Stress and Psychosocial Risks (PRs) are not mentioned explicitly in most pieces of legislation, leading to lack of clarity on the terminology used. To improve the way of dealing with this type of vulnerability and to allow organizations to successfully manage PRs, this work proposes and characterizes a workable problem-solving method in which the PRs can be evaluated for the entropy they generate within the organization. The analysis and development of such a system is based on a series of logical formalisms for Knowledge Representation and Reasoning that are grounded on Logic Programming, complemented with an Artificial Neural Network approach to computing.

Over the last decades a number of guidelines have been proposed for best practices, frameworks, and cyber risk assessment in present computational environments. In order to improve cyber security vulnerability, in this work it is proposed and characterized a feasible methodology for problem solving that allows for the evaluation of cyber security in terms of an estimation of its entropic state, i.e., a predictive evaluation of its risk and vulnerabilities, or in other words, the cyber security level of such ecosystem. The analysis and development of such a model is based on a line of logical formalisms for Knowledge Representation and Reasoning, consistent with an Artificial Neural Networks approach to computing, a model that considers the cause behind the action.

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Cardio Vascular Disease (CVD) also known as heart and circulatory disease comprises all the illnesses of the heart and the circulatory system, namely coronary heart disease, angina, heart attack, congenital heart disease or stroke. CVDs are, nowadays, one of the main causes of death. Indeed, this fact reveals the centrality of prevention and how important is to be aware on these kind of situations. Thus, this work will focus on the development of a decision support system to help to prevent these events from happening, centred on a formal framework based on Mathematical Logic and Logic Programming for Knowledge Representation and Reasoning, complemented with a Case Based Reasoning approach to computing that caters to the handling of incomplete, unknown or even self-contradictory information or knowledge.

The identification, classification and recording of events leading to deterioration of wine quality is essential for developing appropriate strategies avoid them. This work introduces an adverse event reporting and learning system that can help preventing hazards and ensure the quality wines. The Eindhoven Classification Method (ECM) has been extended and adapted to the incidents of the wine industry. Logic Programming was used for Knowledge Representation and Reasoning (KRR) in order to model the universe of discourse, even in the presence of incomplete data, information or knowledge. On the other hand, the evolutionary process of the body of knowledge is to be understood as a process of devaluation, enabling the automatic extraction of knowledge and the generation of reports to identify the most relevant causes of errors lead to a poor wine quality. In addition, the answers to the problem are object of formal evidence through theorem proving.

School dropout is one of the major concerns of our society. Indeed, it is a complex phenomenon, resulting in economic and social losses, either to the individual, family or the community to which the person belongs. Academic difficulty and failure, poor attendance, retention, disengagement from school together with family and socio-economic reasons can lead to such occurrence. In this work Logic Programming was used for knowledge representation and reasoning, letting the modeling of the universe of discourse in terms of defective data, information and knowledge. Artificial Neural Networks were used in order to evaluate potential situations of school dropout and the degree of confidence that one has on such a happening.

GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.

The main purpose of this article is to analyze the impact of the workers’ behavior in terms of their emotions and feelings in system’s performance, i.e., one is looking at issues concerned with Organizational Sustainability. Indeed, one’s aim is to define a process that motivates and inspires managers and personnel to act upon the limit, i.e., to achieve the organizational goals through an effective and efficient implementation of operational and behavioral strategies. The focus will be on the importance of specific psychosocial variables that may affect collective pro-organizational attitudes. Data that is increasing exponentially, and somehow being out of control, i.e., the question is to know the correct value of the information that may be behind these numbers.

The identification, classification and recording of events that may lead to the deterioration of buildings are crucial for the development of appropriate repair strategies. This work presents an extension of the Eindhoven Classification Model to sort adverse events root causes for Building Conservation. Logic Programming was used for knowledge representation and reasoning, letting the modelling of the universe of discourse in terms of defective data, information and knowledge. Indeed, a systematization of the evolution process of the body of knowledge in terms of a new factor, the Quality of Information one, embedded in the Root Cause Analysis was accomplished, i.e., the system proposed led to a process of Quality of Information quantification that permit the study of the event's root causes, on time.